Department of Biostatistics, School of Public Health, Brown University, Providence, Rhode Island.
Center for Evidence Synthesis in Health, School of Public Health, Brown University, Providence, Rhode Island.
Biometrics. 2022 Jun;78(2):624-635. doi: 10.1111/biom.13432. Epub 2021 Feb 11.
We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals with heterogeneous treatment effects in observational data. The CIT algorithms are extensions of the classification and regression tree algorithm that use splitting criteria based on subgroup-specific treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g-formula, and doubly robust estimators of subgroup-specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients.
我们引入因果交互树（CIT）算法，用于在观察性数据中找到具有异质治疗效果的个体亚组。CIT 算法是分类和回归树算法的扩展，它使用基于适用于观察性数据的亚组特异性治疗效果估计量的分裂标准。我们描述了亚组特异性治疗效果的逆概率加权、g 公式和双重稳健估计量，推导出它们的渐近性质，并将其用于构建 CIT 算法的分裂标准。我们在模拟中研究了算法的性能，并将其应用于分析一项评估右心导管术对危重症患者有效性的观察性研究的数据。
